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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Judit Martinez; Eva Costa; P. Herreros; F. Javier Sanchez; Ramon Baldrich |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A Modular and Scalable Architecture for PC-Based Real-Time Vision Systems |
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2003 |
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Real–Time Imaging, (IF: 0.512) |
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9 |
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99-112 |
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CAT @ cat @ MCH2003b |
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394 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Judit Martinez; Eva Costa; P. Herreros; Antonio Lopez; Juan J. Villanueva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
TV-Screen Quality Inspection by Artificial Vision |
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2003 |
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Proceedings SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision (QCAV 2003) |
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A real-time vision system for TV screen quality inspection is introduced. The whole system consists of eight cameras and one processor per camera. It acquires and processes 112 images in 6 seconds. The defects to be inspected can be grouped into four main categories (bubble, line-out, line reduction and landing) although there exists a large variability among each particular type of defect. The complexity of the whole inspection process has been reduced by dividing images into smaller ones and grouping the defects into frequency and intensity relevant ones. Tools such as mathematical morphology, Fourier transform, profile analysis and classification have been used. The performance of the system has been successfully proved against human operators in normal production conditions. |
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Gatlinburg, (EEUU) |
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no |
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ADAS @ adas @ MCH2003a |
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393 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Judit Martinez |
![find record details (via OpenURL) openurl](img/xref.gif)
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Automotive sector and Machine Vision |
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2002 |
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European Integrated Machine Vision, Special Edition: Automative and Glass |
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Admin @ si @ 37316 |
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270 |
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Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired |
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2014 |
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Computer |
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COMP |
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47 |
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4 |
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52-58 |
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Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices. |
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0018-9162 |
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LAMP; |
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no |
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Admin @ si @ TSR2014a |
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2317 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
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Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes |
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2013 |
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Komputer Sapiens |
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KS |
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1 |
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20-25 |
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OR;MV |
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no |
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Admin @ si @ TSR2013 |
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2231 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Robust Head Gestures Recognition for Assistive Technology |
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2014 |
Publication |
Pattern Recognition |
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8495 |
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152-161 |
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This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture. |
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Springer International Publishing |
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0302-9743 |
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978-3-319-07490-0 |
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LAMP; |
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no |
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Admin @ si @ TSR2014b |
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2505 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices |
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Journal Article |
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2016 |
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Neurocomputing |
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NEUCOM |
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175 |
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B |
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866–876 |
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Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices |
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During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset. |
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LAMP; 600.072; 600.068; |
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no |
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Admin @ si @ TRM2016 |
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2721 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Evaluating Real-Time Mirroring of Head Gestures using Smart Glasses |
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Conference Article |
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2015 |
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16th IEEE International Conference on Computer Vision Workshops |
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452-460 |
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Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis. |
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Santiago de Chile; December 2015 |
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ICCVW |
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LAMP; 600.068; 600.072; |
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Admin @ si @ TRM2015 |
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2722 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan Jose Rubio; Takahiro Kashiwa; Teera Laiteerapong; Wenlong Deng; Kohei Nagai; Sergio Escalera; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
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Title |
Multi-class structural damage segmentation using fully convolutional networks |
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2019 |
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Computers in Industry |
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COMPUTIND |
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112 |
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103121 |
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Bridge damage detection; Deep learning; Semantic segmentation |
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Structural Health Monitoring (SHM) has benefited from computer vision and more recently, Deep Learning approaches, to accurately estimate the state of deterioration of infrastructure. In our work, we test Fully Convolutional Networks (FCNs) with a dataset of deck areas of bridges for damage segmentation. We create a dataset for delamination and rebar exposure that has been collected from inspection records of bridges in Niigata Prefecture, Japan. The dataset consists of 734 images with three labels per image, which makes it the largest dataset of images of bridge deck damage. This data allows us to estimate the performance of our method based on regions of agreement, which emulates the uncertainty of in-field inspections. We demonstrate the practicality of FCNs to perform automated semantic segmentation of surface damages. Our model achieves a mean accuracy of 89.7% for delamination and 78.4% for rebar exposure, and a weighted F1 score of 81.9%. |
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HuPBA; no proj |
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Admin @ si @ RKL2019 |
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3315 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan J. Villanueva; Jordi Gonzalez; Javier Varona; Xavier Roca |
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Title |
Aspaces: Action Spaces for Recognition and Synthesis of Human Actions. |
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Miscellaneous |
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2002 |
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II International Workshop Articulated Motion and Deformable Objects AMDO 2002. |
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Palma de Mallorca, Espanya |
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ISE |
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ISE @ ise @ VGV2002 |
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302 |
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Juan J. Villanueva |
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Visualization, Imaging and Image Processing. |
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2002 |
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International Association of Science and Technology for Development. ACTA Press, |
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0–88986–354–3 |
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IASTE |
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ISE @ ise @ Vil2002 |
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276 |
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Juan J. Villanueva |
![find book details (via ISBN) isbn](img/isbn.gif)
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Visualization, Imaging, and Image Processing, |
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2008 |
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Proceedings of the Eight IASTED International Conference |
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ISE @ ise @ Vil2008 |
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1003 |
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Juan Ignacio Toledo; Sounak Dey; Alicia Fornes; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Handwriting Recognition by Attribute embedding and Recurrent Neural Networks |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1038-1043 |
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Handwriting recognition consists in obtaining the transcription of a text image. Recent word spotting methods based on attribute embedding have shown good performance when recognizing words. However, they are holistic methods in the sense that they recognize the word as a whole (i.e. they find the closest word in the lexicon to the word image). Consequently,
these kinds of approaches are not able to deal with out of vocabulary words, which are common in historical manuscripts. Also, they cannot be extended to recognize text lines. In order to address these issues, in this paper we propose a handwriting recognition method that adapts the attribute embedding to sequence learning. Concretely, the method learns the attribute embedding of patches of word images with a convolutional neural network. Then, these embeddings are presented as a sequence to a recurrent neural network that produces the transcription. We obtain promising results even without the use of any kind of dictionary or language model |
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DAG; 600.097; 601.225; 600.121 |
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Admin @ si @ TDF2017 |
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3055 |
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Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
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Conference Article |
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2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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10029 |
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543-552 |
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Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
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The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
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Merida; Mexico; December 2016 |
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Springer International Publishing |
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978-3-319-49054-0 |
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DAG; 600.097; 602.006 |
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Admin @ si @ TSF2016 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados |
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Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model |
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2019 |
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Pattern Recognition |
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86 |
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27-36 |
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Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks |
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Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches. |
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DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 |
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Admin @ si @ TCF2019 |
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3166 |
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